ArticleEBioMedicine2024
Optimal use of β-lactams in neonates: machine learning-based clinical decision support system.
Article in EBioMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in pediatric intensive care units: current applications in sepsis management.World journal of pediatrics : WJP · 2026Pooled it
- Article
- [Application and challenges of the clinical decision support system in hematological diseases].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2026Review
- Development and validation of a machine learning model for individualised meropenem dosing in CRRT patients.Scientific reports · 2026Article
- Machine learning combined with population pharmacokinetics: a hybrid model for predicting the plasma concentration of linezolid in critically ill pediatric patients.Frontiers in pharmacology · 2026Article
- Prediction of Meropenem Target Attainment in Critically Ill Patients Using Routine Clinical Data from a Single Center: A Machine Learning Approach.Infection and drug resistance · 2026Article
- A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications.Clinical and translational science · 2025Article
- Artificial intelligence, medications, pharmacogenomics, and ethics.Pharmacogenomics · 2024Article
- Monitoring of the trough concentration of valproic acid in pediatric epilepsy patients: a machine learning-based ensemble model.Frontiers in pharmacology · 2024Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate prediction of the optimal dose for β-lactam antibiotics in neonatal sepsis is challenging. We aimed to evaluate whether a reliable clinical decision support system (CDSS) based on machine learning (ML) can assist clinicians in making optimal dose selections.
methodsFive β-lactam antibiotics (amoxicillin, ceftazidime, cefotaxime, meropenem and latamoxef), commonly used to treat neonatal sepsis, were selected. The CDSS was constructed by incorporating the drug, patient, dosage, pharmacodynamic, and microbiological factors. The CatBoost ML algorithm was used to build the CDSS. Real-world studies were used to evaluate the CDSS performance. Virtual trials were used to compare the CDSS-optimized doses with guideline-recommended doses.
findingsFor a specific drug, by entering the patient characteristics and pharmacodynamic (PD) target (50%/70%/100% fraction of time that the free drug concentration is above the minimal inhibitory concentration [fT > MIC]), the CDSS can determine whether the planned dosing regimen will achieve the PD target and suggest an optimal dose. The prediction accuracy of all five drugs was >80.0% in the real-world validation. Compared with the PopPK model, the overall accuracy, precision, recall, and F1-Score improved by 10.7%, 22.1%, 64.2%, and 43.1%, respectively. Using the CDSS-optimized doses, the average probability of target concentration attainment increased by 58.2% compared to the guideline-recommended doses.
interpretationAn ML-based CDSS was successfully constructed to assist clinicians in selecting optimal β-lactam antibiotic doses.
fundingThis work was supported by the National Natural Science Foundation of China; Distinguished Young and Middle-aged Scholar of Shandong University; National Key Research and Development Program of China.
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